1 / 43

The craft of Information Visualization

The craft of Information Visualization. NCRM Research Methods Festival 2008 Jonathan C. Roberts School of Computer Science Bangor University. The French engineer, Charles Minard (1781-1870), illustrated the disastrous result of Napoleon's failed Russian campaign of 1812. Minard’s plot.

ouida
Télécharger la présentation

The craft of Information Visualization

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. The craft of Information Visualization NCRM Research Methods Festival 2008 Jonathan C. Roberts School of Computer Science Bangor University

  2. The French engineer, Charles Minard (1781-1870), illustrated the disastrous result of Napoleon's failed Russian campaign of 1812. Minard’s plot • http://www.math.yorku.ca/SCS/Gallery/re-minard.html

  3. One of the first uses of a map to display epidemiological data was this dot chart (from Tufte, 1983, p. 24) by Dr. John Snow (1855) showing deaths from cholera (dots) in relation to the locations of public water pumps. Tufte says, "Snow observed that cholera occurred almost entirely among those who lived near (and drank from) the Broad Street water pump. He had the handle of the contaminated pump removed, ending the neighborhood epidemic which had taken more than 500 lives." The 1854 London Cholera Epidemic.

  4. Advantages of Information Visualization Visualization provides: • The ability to comprehend huge amounts of information • The perception of emergent properties that were not anticipated • problems with the data to be made apparent (e.g. errors or artefacts of the data) • Large/Small scale features can be seen • facilitation of hypothesis formation

  5. Data • Human Pre-processing And transformation Graphics Engine Data manipulation Data gathering Physical Env. Social Env. Schematic of the visualization process

  6. Things to consider… Six important aspects of an Information Visualization: • Data • Visual Structures • Multiple Views • Interaction & Exploration • Tasks (& Management of tasks) • Level & organization

  7. 1. Data & Visual Structures.. • maps interesting data items to graphics objects • Bertin methodology • maps the CONTENT (information to be transmitted - filtered data) to the CONTAINER (the properties of the display/graphic system) using a COMPONENT analysis.

  8. Bertin COMPONENT analysis Bertin’s component analysis • invariant and variational components • number of Components • length of Components • organisation of Components

  9. Bertin CONTAINER - graphic system properties • Main retinal Variables: • Position • Size • Colour (Hue, saturation, value) • Orientation • Shape • Texture • Additional retinal variables • Motion – velocity • Motion – direction • Flicker – frequency • Flicker – phase • Representation Styles • diagrams, networks, maps, symbols • Retinal Variables • Level of organisation • point, line, area, volume

  10. Different Mappings Independent and DependentWhen an experiment is conducted, some variables are manipulated by the experimenter (these are called “independent variables”) and others are measured from the subjects (these are “dependent variables” or “dependent measures.” • 2 variables dependent independent

  11. Different Mappings The values are extra dependent values on the same independent parameter. • 3 ..4 variables dependent independent

  12. The data table… (spreadsheet) • This is ok when there is only one independent variable. But what if we have multiple independents? dependent dependent dependent independent

  13. 2D .. 3D

  14. Multivariate, Car Variable Car1 Car2 MPG 32 43 Weight 1000kg 1100kg Top Speed 130 140 0-60 4 5 Cylinders 8 6

  15. Scatter Plot Matrices Scatter Plot Matrices Reorderable matrix

  16. Parallel-coordinates (PC or ||-coords) • Parallel coordinates yield graphical representations of multi-dimensional relations rather than just finite points sets. • Place the axis parallel and join the dots • Euclidean 3d geometry. X,y,z coordinates • Point in space is given by extents along the axis • ||-coordinates. Point is a line

  17. So what is a point… • A n-d point is equivalent to a line in ||-coordinates http://catt.bus.okstate.edu/jones98/parallel.html

  18. Point line duality l The line is represented by the crossing Line in Euclidean

  19. Cubes.. • Parallel coordinates provides a very simple representation of high dimensional objects such as hypercubes.  • Consider the Parallel coordinate plot of the four corners of a two-dimensional square:

  20. Interacting with ||- Coordinates http://software.fujitsu.com/en/symfoware/visualminer/vmpcddemo.pdf

  21. Selecting a range of records

  22. Selecting records

  23. Verifying a hypothesis

  24. Highlighting relationships

  25. Separating different record groups

  26. Another observation

  27. Visual Structures - Techniques Graphical properties: placing appropriate marks Substitute different properties with different marks Aligning data on different axes composing data Overlaying data on top

  28. Multiple Views • Display different information in different views Same color cdv - Cartographic Visualization for Enumerated Data [Dykes] [Waltz, Roberts]

  29. Dual views – focus+context Table Lens Dual views [Roberts]

  30. Multiple View Techniques • Different views may be better at displaying that information • Correlations between views can be highlighted • Through brushing or zooming • One view can be for Focus another for context (focus+context) • One view can be for Overview another for detail (overview+detail) • Distortion can be used to (say) place more information in a small area

  31. 4. Interaction & Exploration • Allow the user to change their mind and explore the data • To provide sliders/buttons/menus to choose how the data is to be viewed • To select a subset of the information (zoom into this…) • E.g. Brushing • a collection of techniques to dynamically query and directly select elements on the visual display. • Usually in dual views (or more) • Such interaction allows the user to explorethe visualization to interactively select a subset of points and see how these changesare updated in other related views.

  32. Zoom • To focus, Select (or highlight) a feature set of information • Zoom: telephoto-lens, reduced field of view • 3D clipping • Semantic zoom Alternate Representations [Roberts, Ryan]

  33. Dynamic queries • Instant update • Direct manipulation • Sliders/buttons Example of a dynamic queries environment created with IVEE Measurements of heavy metals in Sweden FilmFinder: Ahlberg, Shneiderman

  34. Interaction Techniques Dynamic Queries (indirect manipulation) Direct Manipulation Overlays (e.g. magic lens) Coordination of views which are coordinated? how are they coordinated?

  35. Filter & Extract 1 1 1 1 1 1 2 3 2 1 1 3 9 3 1 1 2 3 2 1 1 1 1 1 1 • Visual extraction • constant quantity of information • brush and highlight • visually altered to stand out (colour, size ...) • sliders (1 < highlight < 4 ...) • Subset (filter) of the data • extract portions of the dataset • Specialize • semi-automatic/manual (seed-point, selection) • neighborhood / global operations 1 1 1 1 1 1 2 3 2 1 1 3 9 3 1 1 2 3 2 1 1 1 1 1 1 1 1 1 1 1 1 2 3 2 1 1 3 9 3 1 1 2 3 2 1 1 1 1 1 1

  36. Filter & Extract 1 1 1 1 1 1 2 3 2 1 1 3 9 3 1 1 2 3 2 1 1 1 1 1 1 • Visual extraction • constant quantity of information • brush and highlight • visually altered to stand out (colour, size ...) • sliders (1 < highlight < 4 ...) • Subset (filter) of the data • extract portions of the dataset • Specialize • semi-automatic/manual (seed-point, selection) • neighborhood / global operations 1 1 1 1 1 1 23 2 1 1 3 9 3 1 1 232 1 1 1 1 1 1 1 1 1 1 1 1 2 3 2 1 1 3 9 3 1 1 2 3 2 1 1 1 1 1 1

  37. Filter & Extract 1 1 1 1 1 1 2 3 2 1 1 3 9 3 1 1 2 3 2 1 1 1 1 1 1 • Visual extraction • constant quantity of information • brush and highlight • visually altered to stand out (colour, size ...) • sliders (1 < highlight < 4 ...) • Subset (filter) of the data • extract portions of the dataset (isolate) • Specialize/Generalize • semi-automatic/manual (seed-point, selection) • neighborhood / global operations 1 1 1 1 1 1 23 2 1 1 3 9 3 1 1 232 1 1 1 1 1 1 3 33 3

  38. 5. Tasks (& Management of tasks) • Foraging for data • Solving problems and investigating hypothesis • Searching for some data (or the lack of data) • Making quantitative/qualitative analysis • Querying and finding evidence for decision making

  39. Techniques to perform the Task Overview Zoom Filter Details on demand Browse Search Read (facts or patterns) Compare Manipulate Explore Create Disseminate and present From. Readings in information visualization - Card/Mackinlay

  40. 6. Level & organization • What is the right level-of-detail? • Are there too many points on display (abstract/summarize/bin/aggregate) • How is the information organized? • Think what is close and what is near • Near objects are easier to compare • E.g. re-order the axes on a ||-coord plot

  41. Techniques for Level Delete Re-order Cluster Class Promote Average Abstract/Summarize Instantiate Extract Compose Organize

  42. Things to remember… Six important aspects of an Information Visualization: • Data • Visual Structures • Multiple Views • Interaction & Exploration • Tasks (& Management of tasks) • Level & organization

  43. The craft of Information Visualization Jonathan C. Roberts School of Computer Science Bangor University END

More Related